In recent years, with the rapid development of deep learning in code generation, text writing, and experimental design, accurately capturing and assessing researchers’ creativity has become a key issue in need of breakthrough. Traditional creativity evaluation methods, being static, subjective, and time-consuming, fail to reflect the dynamic iteration and multidimensional characteristics of creative thinking. To address this, we propose a dynamic creativity evaluation framework (DynaCREA) based on reinforcement learning, featuring an adaptive decision-making and feedback mechanism that utilizes real-time evaluation of user interaction and creativity metrics. Through multi-turn interactions between researchers and large language models, the framework integrates multimodal tasks, including textual contexts, verbal expression, and image-inspired tasks, to achieve real-time quantification of key dimensions of creativity (such as originality, fluency, elaboration, and flexibility). The intelligent agent leverages immediate feedback to adaptively adjust the design of subsequent tasks, thereby forming a novel creativity evaluation method that is both theoretically rigorous and practically efficient. Preliminary experimental results show that, after training, the intelligent agent meets a high degree of consistency with human evaluators across all indicators, demonstrating broad prospects for application in complex research environments.